# Prompt Patterns for Developers
AI coding assistants aren’t magic. They’re tools that respond to input—and your input determines whether you waste time iterating or get working code in seconds. Most developers treat prompts like chat messages. They’re treating a precision tool like a casual conversation.
This article covers six prompt patterns that actually work in real development workflows. You’ll see the exact prompt structures, when to use each, and why some patterns outperform others. No hype. Just patterns you can copy into your workflow today.
—
## The Zero-Shot Pattern
The zero-shot pattern is the baseline: ask without examples. It works for straightforward tasks where the model’s training covers the domain.
“`python
# Zero-shot: direct request
“Write a Python function that validates an email address using regex.
Return True if valid, False otherwise. Include type hints.”
“`
This pattern works when the task is well-defined and common. The model knows what email validation looks like from its training data. You don’t need to show examples.
**When to use it:**
– Simple, well-documented tasks
– Generating boilerplate code
– Explaining concepts
– Quick one-off queries
**Limitations:** Zero-shot fails when your requirements diverge from common patterns. If you need a specific coding style, unusual structure, or niche domain knowledge, you’ll need more context.
—
## The Few-Shot Pattern
Few-shot provides 2-3 examples of the input/output format you want. This dramatically improves consistency for tasks where structure matters.
“`python
# Few-shot: include examples
“””
Convert these API responses into typed dataclasses:
Input: {“user_id”: 123, “name”: “Alice”, “created_at”: “2026-01-15”}
Output:
@dataclass
class User:
user_id: int
name: str
created_at: str
Input: {“order_id”: “ORD-456”, “total”: 99.99, “items”: [“a”, “b”]}
Output:
“””
# The model continues the pattern
“`
The examples train the model on your expected format without explicit instructions. You’re showing, not telling.
**When to use it:**
– Generating code in a specific style
– Creating consistent output formats
– Working with custom data structures
– Any task where zero-shot produces inconsistent results
**Why it works:** Examples anchor the model’s output to your exact requirements. The pattern matching in few-shot is more reliable than description-based instructions.
—
## The Chain-of-Thought Pattern
Chain-of-thought asks the model to reason step-by-step before giving the final answer. This works for complex problems where the model might otherwise jump to a wrong conclusion.
“`python
# Chain-of-thought with explicit reasoning request
“””
Debug this code. First identify the bug, then explain the root cause,
then provide the fix.
Error: TypeError: ‘NoneType’ object is not iterable
Code:
def process_users(user_ids):
for user_id in user_ids:
user = get_user(user_id)
if user:
print(user.name)
results = []
for user in user_ids:
results.append(user.name) # Line 12 – this is where it fails
return results
“””
“`
**When to use it:**
– Debugging complex issues
– Architectural decisions
– Multi-step refactoring
– Any problem where the solution path matters
**The internal mechanism:** Models don’t actually “think” — but prompting for reasoning activates different parts of their training. The model generates tokens that explain the problem space, which often leads to more accurate final outputs. You’re not getting reasoning, but you’re getting better token prediction.
—
## The Role-Based Pattern
Assign the model a specific persona. This primes the model to use relevant knowledge and terminology.
“`python
# Role-based: security expert
“””
Act as a senior security engineer. Review this authentication code
for vulnerabilities. Focus on:
– SQL injection risks
– Password handling
– Session management
Code:
def login(username, password):
query = f”SELECT * FROM users WHERE username = ‘{username}'”
# … executes raw SQL
“””
“`
“`python
# Role-based: performance specialist
“””
You are a database performance engineer. Optimize this query for
tables with 10M+ rows. Consider indexing strategies and query structure.
SELECT * FROM orders o
JOIN customers c ON o.customer_id = c.id
WHERE c.status = ‘active’ AND o.created_at > ‘2026-01-01’
“””
“`
**When to use it:**
– Domain-specific reviews
– Specialized code generation
– Explaining complex concepts
– Getting expert-level feedback
**Why it works:** The role acts as a context filter. A security persona activates different training patterns than a general coding persona. You’re steering which parts of the model’s knowledge get used.
—
## The Template Pattern
Templates are reusable prompt structures for recurring tasks. Save these as snippets in your IDE or a separate file.
“`python
# Template: code review prompt
REVIEW_TEMPLATE = “””
Act as a {seniority} engineer reviewing {language} code.
Focus area: {focus_area}
Code to review:
“`{language}
{code}
“`
Provide:
1. Issues found (severity: high/medium/low)
2. Suggested fixes
3. Overall assessment (approve/needs changes/major concerns)
“””
# Usage
prompt = REVIEW_TEMPLATE.format(
seniority=”senior”,
language=”python”,
focus_area=”error handling”,
code=open(“handler.py”).read()
)
“`
**When to use it:**
– Repeated task types
– Team-wide prompt standardization
– Complex prompts with multiple variables
**Practical implementation:** I keep a `prompts/` directory with template files. For code reviews, refactoring, test generation — each has a template I customize per task. This beats rethinking the prompt each time.
—
## The Persona-Constraints Pattern
Combine role assignment with explicit constraints. This gives you expert guidance within boundaries you define.
“`python
# Persona + constraints
“””
Act as a backend architect designing a REST API.
Constraints:
– Use Python with FastAPI
– Maximum 3 endpoints
– Include authentication but no external auth providers
– Must handle rate limiting
Domain: E-commerce inventory management
Provide the endpoint design, data models, and key implementation details.
“””
“`
**When to use it:**
– Architectural planning
– Technology-constrained generation
– Getting expert advice within your stack
**Why it’s effective:** Constraints prevent the model from defaulting to generic solutions. You’re getting expert knowledge filtered through your specific requirements.
—
## Key Takeaways
– Match the pattern to the task: zero-shot for simple tasks, few-shot for structured output, chain-of-thought for complex reasoning
– Be specific about requirements — the model can’t read your mind, but it can follow examples precisely
– Build a personal template library for recurring tasks; prompts are code too
– Combine patterns when needed: role + constraints + chain-of-thought for multi-dimensional problems
– Test prompts like code: if it doesn’t work, iterate rather than assume the tool is broken
—
## Next Steps
1. Pick one pattern from this article — try few-shot for your next code generation task
2. Create a template file for your most common prompt types (reviews, refactoring, tests)
3. Track which patterns produce usable results vs. which need iteration
4. Share effective prompts with your team; standardize what works
The difference between developers who waste hours with AI and those who save hours isn’t the tool. It’s knowing how to ask.